Content-based Graph Reconstruction for Cold-start Item Recommendation - SIGIR 2024
Association for Computing Machinery (ACM) via YouTube
AI Engineer - Learn how to integrate AI into software applications
Learn AI, Data Science & Business — Earn Certificates That Get You Hired
Overview
AI, Data Science & Cloud Certificates from Google, IBM & Meta — 40% Off
One plan covers every Professional Certificate on Coursera. 40% off Coursera Plus Annual.
Unlock All Certificates
Explore a 13-minute conference talk from SIGIR 2024 that delves into content-based graph reconstruction for cold-start item recommendation. Learn about innovative approaches presented by authors Jinri Kim, Eungi Kim, Kwangeun Yeo, Yujin Jeon, Chanwoo Kim, Sewon Lee, and Joonseok Lee as they address challenges in recommender systems. Gain insights into graph-based techniques and their application in solving the cold-start problem, a common issue in recommendation algorithms for new items with limited user interaction data.
Syllabus
SIGIR 2024 T1.4 [fp] Content-based Graph Reconstruction for Cold-start item recommendation
Taught by
Association for Computing Machinery (ACM)